Book Image

Mastering Azure Machine Learning. - Second Edition

By : Christoph Körner, Marcel Alsdorf
Book Image

Mastering Azure Machine Learning. - Second Edition

By: Christoph Körner, Marcel Alsdorf

Overview of this book

Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project life cycle that ML professionals, data scientists, and engineers can use in their day-to-day workflows. This book covers the end-to-end ML process using Microsoft Azure Machine Learning, including data preparation, performing and logging ML training runs, designing training and deployment pipelines, and managing these pipelines via MLOps. The first section shows you how to set up an Azure Machine Learning workspace; ingest and version datasets; as well as preprocess, label, and enrich these datasets for training. In the next two sections, you'll discover how to enrich and train ML models for embedding, classification, and regression. You'll explore advanced NLP techniques, traditional ML models such as boosted trees, modern deep neural networks, recommendation systems, reinforcement learning, and complex distributed ML training techniques - all using Azure Machine Learning. The last section will teach you how to deploy the trained models as a batch pipeline or real-time scoring service using Docker, Azure Machine Learning clusters, Azure Kubernetes Services, and alternative deployment targets. By the end of this book, you’ll be able to combine all the steps you’ve learned by building an MLOps pipeline.
Table of Contents (23 chapters)
1
Section 1: Introduction to Azure Machine Learning
5
Section 2: Data Ingestion, Preparation, Feature Engineering, and Pipelining
11
Section 3: The Training and Optimization of Machine Learning Models
17
Section 4: Machine Learning Model Deployment and Operations

Chapter 7: Advanced Feature Extraction with NLP

In the previous chapters, we learned about many standard transformation and preprocessing approaches within the Azure Machine Learning service as well as typical labeling techniques using the Azure Machine Learning Data Labeling service. In this chapter, we want to go one step further to extract semantic features from textual and categorical data—a problem that users often face when training ML models. This chapter will describe the foundations of feature extraction with Natural Language Processing (NLP). This will help you to practically implement semantic embeddings using NLP for your ML pipelines.

First, we will take a look at the differences between textual, categorical, nominal, and ordinal data. This classification will help you to decide the best feature extraction and transformation technique per feature type. Later, we will look at the most common transformations for categorical values, namely label encoding and one...